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http://dx.doi.org/10.5370/JEET.2017.12.4.1548

Analysis of Neural Network Approaches for Nonlinear Modeling of Switched Reluctance Motor Drive  

Saravanan, P (Dept. of Electrical and Electronics Engineering, SSN College of Engineering)
Balaji, M (Dept. of Electrical and Electronics Engineering, SSN College of Engineering)
Balaji, Nagaraj K (Dept. of Electrical and Electronics Engineering, SSN College of Engineering)
Arumugam, R (Dept. of Electrical and Electronics Engineering, SSN College of Engineering)
Publication Information
Journal of Electrical Engineering and Technology / v.12, no.4, 2017 , pp. 1548-1555 More about this Journal
Abstract
This paper attempts to employ and investigate neural based approaches as interpolation tools for modeling of Switched Reluctance Motor (SRM) drive. Precise modeling of SRM is essential to analyse the performance of control strategies for variable speed drive application. In this work the suitability of Generalized Regression Neural Network (GRNN) and Extreme Learning Machine (ELM) in addition to conventional neural network are explored for improving the modeling accuracy of SRM. The neural structures are trained with the data obtained by modeling of SRM using Finite Element Analysis (FEA) and the trained neural network is incorporated in the model of SRM drive. The results signify the modeling accuracy with GRNN model. The closed loop drive simulation is performed in MATLAB/Simulink environment and the closeness of the results in comparison with the experimental prototype validates the modeling approach.
Keywords
Switched reluctance motor; Finite element analysis; ANN; GRNN; ELM;
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